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arXiv · 2507.13607

Efficient Burst Super-Resolution with One-step Diffusion

Kento Kawai·Takeru Oba·Kyotaro Tokoro·Kazutoshi Akita·Norimichi Ukita
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Abstract

While burst Low-Resolution (LR) images are useful for improving their Super Resolution (SR) image compared to a single LR image, prior burst SR methods are trained in a deterministic manner, which produces a blurry SR image. Since such blurry images are perceptually degraded, we aim to reconstruct sharp and high-fidelity SR images by a diffusion model. Our method improves the efficiency of the diffusion model with a stochastic sampler with a high-order ODE as well as one-step diffusion using knowledge distillation. Our experimental results demonstrate that our method can reduce the runtime to 1.6 % of its baseline while maintaining the SR quality measured based on image distortion and perceptual quality.

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Kento Kawai, Takeru Oba, Kyotaro Tokoro, Kazutoshi Akita, Norimichi Ukita. 2025-07-18. Efficient Burst Super-Resolution with One-step Diffusion. https://arxiv.org/abs/2507.13607

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